To test in detail how AgentNet performance depends on the number of agents and steps in large real-world graphs we perform an ablation on the DD dataset (Figure REF ). We see that many configurations perform well, even when just a fraction of the graph is explored. We also observe that unlike with traditional GNNs [1]}, [2]}, using more steps (layers) does not cause a noticeable decrease in performance.
Especially when fewer than \(n\)  agents are used (\(n\approx 284\) ). This potentially indicates higher robustness to oversquashing and oversmoothing compared to traditional GNNs.
